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Neural Networks Training Courses

Neural Networks courses

Client Testimonials

Applied Machine Learning

ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Artificial Neural Networks, Machine Learning, Deep Thinking

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease - Knowledgepool Group Ltd

Neural Networks Course Outlines

This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
Current state of the technology
What is used
What may be potentially used
Rules based AI
Simplifying decision
Machine Learning
Classification
Clustering
Neural Networks
Types of Neural Networks
Presentation of working examples and discussion
Deep Learning
Basic vocabulary
When to use Deep Learning, when not to
Estimating computational resources and cost
Very short theoretical background to Deep Neural Networks
Deep Learning in practice (mainly using TensorFlow)
Preparing Data
Choosing loss function
Choosing appropriate type on neural network
Accuracy vs speed and resources
Training neural network
Measuring efficiency and error
Sample usage
Anomaly detection
Image recognition
ADAS

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Introduction to Applied Machine Learning
Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off
Regression
Linear regression
Generalizations and Nonlinearity
Exercises
Classification
Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises
Cross-validation and Resampling
Cross-validation approaches
Bootstrap
Exercises
Unsupervised Learning
K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means

This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Ridge regression
Clustering

This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization.
The purpose is to give practical applications to Machine Learning to participants interested in applying the methods at work.
Sector specific examples are used to make the training relevant to the audience.
Naive Bayes
Multinomial models
Bayesian categorical data analysis
Discriminant analysis
Linear regression
Logistic regression
GLM
EM Algorithm
Mixed Models
Additive Models
Classification
KNN
Bayesian Graphical Models
Factor Analysis (FA)
Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
Support Vector Machines (SVM) for regression and classification
Boosting
Ensemble models
Neural networks
Hidden Markov Models (HMM)
Space State Models
Clustering

Audience
If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you.
It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing.
It is not aimed at people configuring the solution, those people will benefit from the big picture though.
Delivery Mode
During the course delegates will be presented with working examples of mostly open source technologies.
Short lectures will be followed by presentation and simple exercises by the participants
Content and Software used
All software used is updated each time the course is run so we check the newest versions possible.
It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.
Quick Overview
Data Sources
Minding Data
Recommender systems
Target Marketing
Datatypes
Structured vs unstructured
Static vs streamed
Attitudinal, behavioural and demographic data
Data-driven vs user-driven analytics
data validity
Volume, velocity and variety of data
Models
Building models
Statistical Models
Machine learning
Data Classification
Clustering
kGroups, k-means, nearest neighbours
Ant colonies, birds flocking
Predictive Models
Decision trees
Support vector machine
Naive Bayes classification
Neural networks
Markov Model
Regression
Ensemble methods
ROI
Benefit/Cost ratio
Cost of software
Cost of development
Potential benefits
Building Models
Data Preparation (MapReduce)
Data cleansing
Choosing methods
Developing model
Testing Model
Model evaluation
Model deployment and integration
Overview of Open Source and commercial software
Selection of R-project package
Python libraries
Hadoop and Mahout
Selected Apache projects related to Big Data and Analytics
Selected commercial solution
Integration with existing software and data sources

The training is aimed at people who want to learn the basics of neural networks and their applications.
The Basics
Whether computers can think of?
Imperative and declarative approach to solving problems
Purpose Bedan on artificial intelligence
The definition of artificial intelligence. Turing test. Other determinants
The development of the concept of intelligent systems
Most important achievements and directions of development
Neural Networks
The Basics
Concept of neurons and neural networks
A simplified model of the brain
Opportunities neuron
XOR problem and the nature of the distribution of values
The polymorphic nature of the sigmoidal
Other functions activated
Construction of neural networks
Concept of neurons connect
Neural network as nodes
Building a network
Neurons
Layers
Scales
Input and output data
Range 0 to 1
Normalization
Learning Neural Networks
Backward Propagation
Steps propagation
Network training algorithms
range of application
Estimation
Problems with the possibility of approximation by
Examples
XOR problem
Lotto?
Equities
OCR and image pattern recognition
Other applications
Implementing a neural network modeling job predicting stock prices of listed
Problems for today
Combinatorial explosion and gaming issues
Turing test again
Over-confidence in the capabilities of computers

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